MITT: Medical Image Tracking Toolbox

Abstract

Over the years, medical image tracking has gained considerable attention from both medical and research communities due to its widespread utility in a multitude of clinical applications, from functional assessment during diagnosis and therapy planning to structure tracking or image fusion during image-guided interventions. Despite the ever-increasing number of image tracking methods available, most still consist of independent implementations with specific target applications, lacking the versatility to deal with distinct end-goals without the need for methodological tailoring and/or exhaustive tuning of numerous parameters. With this in mind, we have developed the medical image tracking toolbox (MITT)-a software package designed to ease customization of image tracking solutions in the medical field. While its workflow principles make it suitable to work with 2-D or 3-D image sequences, its modules offer versatility to set up computationally efficient tracking solutions, even for users with limited programming skills. MITT is implemented in both C/C++ and MATLAB, including several variants of an object-based image tracking algorithm and allowing to track multiple types of objects (i.e., contours, multi-contours, surfaces, and multi-surfaces) with several customization features. In this paper, the toolbox is presented, its features discussed, and illustrative examples of its usage in the cardiology field provided, demonstrating its versatility, simplicity, and time efficiency.

Strain maps of the left atrium imaged with a novel high-resolution CINE MRI protocol

“…For each short-axis slice which included the left atrium, the segmentation contour (corresponding to the LA endocardial wall) was approximated using cubic splines to obtain 150 evenly spaced control points. The position of each of these points in previous and subsequent cardiac phases was estimated, in 3D, using the Medical Image Tracking Toolbox, MITT [17]. Very briefly, MITT employs an iterative image tracking algorithm, called localized anatomical affine optical flow (AAOF), to estimate the motion and deformation of the object of interest across an image sequence.…”

A MatLab-Based Mean-Field-Type Games Toolbox: Continuous-Time Version

“…Toolboxes have been developed in several different fields and have considerably improved the research pace. Among all the different types of tools we can find the following: tools that allow designing or modeling in a systematic manner, e.g., the modeling of joint contact mechanics [16], tools that facilitate the plot of results, e.g., the generation of medical images [17], and/or tools allowing the analysis of systems' performance [18]. In particular, the toolbox that is proposed in this paper concerns to the game, learning and automatic control theory as presented next.…”

A Robust Object Tracking Method for Surveillance Applications to Handle Occlusion

“…Vision-based Various machine vision algorithms for intelligent surveillance systems comprises of object detection and object tracking as its fundamental and crucial part, that led the realization of high-level and complex systems. The applications of object tracking are not only limited exclusively to machine vision like biomedical imaging [1], video security systems [2] and vision-based traffic monitoring [3], but it is further extended to more highly integrated systems including such as human computer interaction [4], navigation systems [5], robotic vision [6] and anomaly detection [7]. However to develop a robust and persistent visual object tracking framework is still a contemporary problem due to the challenging factors such as occlusion, in-plane rotation, out-of-plane rotation, background clutters, uneven motion, variation of scale and illumination.…”